Title: Metamodeling and On-Line Clustering for Loss-of-Flow Accident Precursors Identification in a Superconducting Magnet Cryogenic Cooling Circuit

Journal: Energies

Authors:

Vincenzo Destino, Nicola Pedroni, Roberto Bonifetto, Francesco Di Maio, Laura Savoldi and Enrico Zio

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/17/5552/pdf

Abstract:

In the International Thermonuclear Experimental Reactor, plasma is magnetically confined with Superconductive Magnets (SMs) that must be maintained at the cryogenic temperature of 4.5 K by one or more Superconducting Magnet Cryogenic Cooling Circuits (SMCCC). To guarantee cooling, Loss-of-Flow Accidents (LOFAs) in the SMCCC are to be avoided. In this work, we develop a three-step methodology for the prompt detection of LOFA precursors (i.e., those combinations of component failures causing a LOFA). First, we randomly generate accident scenarios by Monte Carlo sampling of the failures of typical SMCCC components and simulate the corresponding transient system response by a deterministic thermal-hydraulic code. In this phase, we also employ quick-running Proper Orthogonal Decomposition (POD)-based Kriging metamodels, adaptively trained to reproduce the output of the long-running code, to decrease the computational time. Second, we group the generated scenarios by a Spectral Clustering (SC) employing the Fuzzy C-Means (FCM), in order to identify the main patterns of system evolution towards abnormal states (e.g., a LOFA). Third, we develop an On-line Supervised Spectral Clustering (OSSC) technique to associate time-varying parameters measured during plant functioning to one of the prototypical groups obtained, which may highlight the related LOFA precursors (in terms of SMCCC components failures). We apply the proposed technique to the simplified model of a cryogenic cooling circuit of a single module of the ITER Central Solenoid Magnet (CSM). The framework developed promptly detects 95% of LOFA events and around 80% of the related precursors.

Title: A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System

Journal: Reliability Engineering & System Safety

Authors: L. Puppo, N. Pedroni, Francesco di Maio, A. Bersano, C. Bertani, Enrico Zio

This is an open-access article that can be downloaded from the following link before October 17: https://authors.elsevier.com/c/1dfEy3OQ~fVt7D

Abstract:

In the safety analyses of passive systems for nuclear energy applications, computationally demanding models can be substituted by fast-running surrogate models coupled with adaptive sampling techniques; for speeding up the exploration of the components and system state-space and the characterization of the conditions leading to failure (i.e., the system Critical failure Regions, CRs). However, in some cases of non-smoothness and multimodality of the state-space, the existing approaches do not suffice. In this paper, we propose a novel methodological framework, based on Finite Mixture Models (FMMs) and Adaptive Kriging (AK-MCS) for CRs characterization in case of non-smoothness and/or multimodality of the output. The framework contains three main steps: 1) dimensionality reduction through FMMs to tackle the output non-smoothness and multimodality, while focusing on its clusters defining the system failure; 2) adaptive training (AK-MCS) of the metamodel on the reduced space to mimic the time-demanding model and, finally, 3) use of the trained metamodel provide the output for new input combinations and retrieve information about the CRs.

The framework is applied to the case study of a generic Passive Safety System (PSS) for Decay Heat Removal (DHR) designed for advanced Nuclear Power Plants (NPPs). The PSS operation is modelled through a time-demanding Thermal-Hydraulic (T-H) model and the pressure selected for characterizing the PSS response to accidental conditions shows a strong non-smooth and multimodal behavior. A comparison with an alternative approach of literature relying on the use of Support Vector Classifier (SVC) to cluster the output domain is presented to support the framework as a valid approach in challenging CRs characterization.

Title: Accounting for Safety Barriers Degradation in the Risk Assessment of Oil and Gas Systems by Multistate Bayesian Networks

Journal: Reliability Engineering & System Safety

Authors: Francesco di Maio, O. SCAPINELLO, Enrico Zio, C. CIARAPICA, S. CINCOTTA, A. CRIVELLARI, L. DECARLI, L. LAROSA

This is an open-access article that can be downloaded from the following link before September 22: https://authors.elsevier.com/a/1dWEL3OQ%7EfVt4Z

Abstract:

In this paper, a multistate Bayesian Network (BN) is proposed to model and evaluate the functional performance of safety barriers in Oil and Gas plants. The nodes of the BN represent the safety barriers Health States (HSs) and the corresponding conditional Failure Probability (FP) values are assigned. HSs are assessed on the basis of specific Key Performance Indicators (KPIs) related to the barrier characteristics (i.e., technical, procedural or organizational, continuously monitored or event-based characterized). FP values are estimated from failure datasets (for technical barriers), evaluated by Human Reliability Analysis (HRA) (for operational and organizational barriers) and assigned by expert elicitation (for barriers lacking data or information). For illustration, the multistate BN model is developed for preventive barriers and applied to a case study related to the potential release of flammable material in the slug catcher of a representative O&G Upstream plant which may lead to major accident scenarios (fire, explosion, toxic dispersion). The results from the case study demonstrate that the multistate BN model is able to account for the safety barriers HS and their associated functional performance.

Title: Multi-State Reliability Assessment Model of Base-Load Cyber-Physical Energy Systems (CPES) during Flexible Operation Considering the Aging of Cyber Components

Journal: Energies

Authors: Zhaojun Hao, Francesco di Maio, Enrico Zio

This is an open-access article that can be downloaded from the following link: https://www.mdpi.com/1996-1073/14/11/3241/htm

Abstract: Cyber-Physical Energy Systems (CPESs) are energy systems which rely on cyber components for energy production, transmission and distribution control, and other functions. With the penetration of Renewable Energy Sources (RESs), CPESs are required to provide flexible operation (e.g., load-following, frequency regulation) to respond to any sudden imbalance of the power grid, due to the variability in power generation by RESs. This raises concerns on the reliability of CPESs traditionally used as base-load facilities, such as Nuclear Power Plants (NPPs), which were not designed for flexible operation, and more so, since traditionally only hardware components aging and stochastic failures have been considered for the reliability assessment, whereas the contribution of the degradation and aging of the cyber components of CPSs has been neglected. In this paper, we propose a multi-state model that integrates the hardware components stochastic failures with the aging of cyber components, and quantify the unreliability of CPES in load-following operations under normal/emergency conditions. To show the application of the reliability assessment model, we consider the case of the Control Rod System (CRS) of a NPP typically used for a base-load energy supply.

Title: Failure identification in a nuclear passive safety system by Monte Carlo simulation with adaptive Kriging

Journal: Nuclear Engineering and Design

Authors: Puppo, L., Pedroni, N., Bersano, A., Di Maio, F., Bertani, C., Zio, E.

This is an open-access article that can be downloaded from the following link before July 22, 2021: https://authors.elsevier.com/c/1dAUO6j-yo1ve

Abstract: Passive Safety Systems (PSSs) are increasingly employed in advanced Nuclear Power Plants (NPPs). Their safety performance is evaluated through computationally expensive Thermal-Hydraulic (T-H) simulations models and the identification of the operational conditions which lead to unsafe conditions (the so-called Critical failure Regions, CRs) may be challenging.

In the present paper, a computational framework is proposed to identify the CRs of a generic passive Decay Heat Removal (DHR) system of a NPP. A time-demanding Best-Estimate Thermal-Hydraulic (BE-TH) model of the system is used to train a fast-running metamodel embedded within an adaptive sampling technique of literature, namely Adaptive Kriging Monte Carlo Sampling (AK-MCS), so as to provide increased accuracy in proximity of the failure threshold and identify which input values lead the PSS to failure. To the best authors’ knowledge this is the first time that the metamodel-based AK-MCS technique is applied for the identification of the CRs of a PSS of an NPP.